Abstract
Corpus-based researchers and traditional qualitative researchers, such as those interested in critical discourse analysis, are often required to select prototypical texts for close reading that include the language features of interest that are present in a much larger corpus. Traditional approaches to this selection procedure have been largely ad hoc. In this paper, we offer a more principled way of selecting texts for close reading based on a ranking of texts in terms of the number of keywords they contain. To facilitate this analysis, we have developed a multiplatform, freeware software tool called ProtAnt that analyses the texts, generates a ranked list of keywords based on statistical significance and effect size, and then orders the texts by the number of keywords in them. We describe various experiments that demonstrate the ProtAnt analysis is effective not only at identifying prototypical texts, but also identifying outlier texts that may need to be removed from a target corpus.
Highlights
Corpus-based researchers and traditional qualitative researchers are often required to select texts for close reading that include the language features of interest present in a much larger corpus
We propose a more principled way of selecting texts for close reading based on a ranking of texts in terms of the number of keywords they contain
Our experiments with ProtAnt confirm the utility of identifying prototypical texts using the concept of keywords, with results largely matching predicted outcomes
Summary
Corpus-based researchers and traditional qualitative researchers are often required to select texts for close reading that include the language features of interest present in a much larger corpus. We propose a more principled way of selecting texts for close reading based on a ranking of texts in terms of the number of keywords (unusually frequent words in the target corpus compared with a reference corpus) they contain. To facilitate this analysis, we have developed a multiplatform, freeware software tool called ProtAnt (Anthony & Baker 2015) that analyses the texts, generates a ranked list of keywords based on statistical significance and effect size, and orders the texts by the number of keywords in them.
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